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Breaking the latency barrier: convverse.ai’s journey to an ultra-fast real-time conversation engine

A detailed look at how convverse.ai engineered an ultra-fast real-time conversation engine, reducing latency, improving response accuracy, and powering next-gen sales enablement.

AI FOR SALES

Mukesh Kumar, Founder of convverse.ai

12/7/20254 min read

When building AI systems for real-time enterprise conversations, accuracy alone is not enough speed becomes a feature. A response delivered after 20 seconds in the middle of a live sales call isn’t helpful, even if it’s correct. At convverse.ai, this realization kicked off a long engineering journey: how do we build an intelligent system that responds at human speed?

What began as an early prototype evolved into a system capable of reliably answering contextual user queries in 2–5 seconds, even under multi-tenant load and never exceeding 10 seconds under worst-case conditions. The shift wasn’t just about using a faster model, it required rethinking everything: retrieval strategy, architecture, inference providers, error handling, prompt design, and compute choices.

From CAG to RAG: The Turning Point

Our earliest implementation relied on a Context-Augmented Generation (CAG) approach: pass the full document (sometimes entire PDFs) to the LLM and ask it to reason over everything. This approach ensured accuracy hallucination was rare because the model always had full context.

But speed? Terrible.

Our first production numbers averaged 20–25 seconds per query. For a conversational interface, that delay was unacceptable.

The breakthrough happened when we migrated to a Retrieval-Augmented Generation (RAG) architecture instead of passing entire documents, we:

Chunked documents
Generated embeddings
Stored them in Supabase pgvector
Retrieved only the relevant sections during inference

This architectural shift alone reduced latency to 4–5 seconds, with best cases at 2–3 seconds.

The challenge wasn’t just transporting data fast it was building an indexing and storage layer capable of storing embeddings for multiple enterprise clients while still returning results quickly. Using metadata filtering, strict tenant boundaries, and optimized vector similarity search, we achieved low-latency retrieval even at scale.

But RAG Isn’t Perfect And That’s Where Fallback Matters

RAG depends on retrieval. If the right chunk isn’t retrieved, the output collapses or worse, the model confidently hallucinates. For an enterprise-grade product, neither is acceptable.

So, we introduced confidence evaluation. If the answer generated using retrieved context lacks grounding signals, the system automatically falls back to CAG mode, passing a larger context window or full document to the model.

This gives us the best of both worlds:

Scenario Strategy

Clear retrieval match RAG (fast, economical, reliable)

Retrieval uncertainty CAG fallback (slower but accurate)

Reliability became non-negotiable especially when live users are waiting.

The concepts behind fallback routing, retrieval grounding, and accuracy-based model switching are now core topics in modern Generative AI and Agentic AI programs, such as those taught at the Boston Institute of Analytics and for good reason: these are now foundational skills, not advanced techniques.

Choosing the Right LLM Is a Practical, Not Philosophical Decision

Model selection wasn’t about hype it was about benchmarks.

We built custom evaluation sets across:

factual recall
grounding accuracy
latency performance
hallucination probability
reasoning under missing context

The balance we needed was: fast enough for live conversation, but accurate enough to trust.

After testing multiple models across providers, the winner was:

GPT-OSS-120B running on Groq hardware acceleration.

This model provided the right equilibrium of speed, reasoning ability and cost. For longer tasks where latency is less critical like generating summaries or “next-step” breakdowns we route to OpenAI’s GPT-5 for improved depth and reasoning stability.

What this process made clear is that selecting the right model isn’t guesswork anymore. It requires structured evaluation, real-world validation, and understanding trade-offs between inference speed, reasoning depth, and retrieval behavior. This kind of thinking is becoming foundational in modern AI engineering education, and programs like the Generative and Agentic AI curriculum at the Boston Institute of Analytics are beginning to formalize these evaluation frameworks for the next generation of builders.

Model Providers Matter More Than Expected

Choosing the right LLM isn’t enough the provider powering inference impacts both experience and engineering complexity.

Today our stack uses:

Groq → real-time inference, extremely fast token speed
OpenAI GPT-5 → high-accuracy, long-context workloads
Cerebras → under exploration for future performance gains

This multi-provider routing ensures flexibility and resilience.

The Infrastructure Behind the Speed

Latency improvements didn’t come from a single trick they came from architectural alignment.

We shifted from a pure server deployment to a hybrid compute approach:

EC2 handles continuous workloads like transcript processing and question detection
AWS Lambda powers individual retrieval and inference requests, scaling instantly with load

This ensured we weren’t paying for idle compute, and users never waited due to concurrency bottlenecks.

On top of that, building the backend using native async execution (instead of threaded or blocking patterns) ensured we could handle high-volume parallel inference with predictable timing.

Engineering Decisions That Became Game-Changers

Some improvements weren’t architectural they were operational:

Strict prompt engineering dramatically reduced hallucinations; every output format and constraint is explicitly defined.
Emoji-based structured logs turned debugging (especially across distributed compute) into a human-scannable process.
Yes sometimes 🚨 beats a 400-word message.
Error handling paranoia saved us during a Groq service disruption instead of leaving users watching a spinning loader, we displayed meaningful error codes.
Batching requests where possible helped reduce redundant inference calls and boosted throughput efficiency.

Conclusion: The Journey Isn’t Over

Building real-time AI isn’t just about modeling it’s about systems thinking. Retrieval architecture, inference providers, prompt structure, compute strategy, and failure planning together define speed and reliability.

convverse.ai didn’t simply optimize a model it engineered the ecosystem around it.

Because in real-world AI: Speed isn’t the enemy of intelligence it’s part of the definition.

But building cutting-edge, real-time AI applications also requires the right guidance, the right foundations, and exposure to the evolving landscape of AI systems. As the field transitions from simple prompt-based interfaces to complex agentic and retrieval-powered systems, having structured learning becomes invaluable.

This is where well-designed education programs matter.

The Generative AI and Agentic AI development program offered by Boston Institute of Analytics (BIA) is one such example. Their curriculum goes beyond just theory and focuses on end-to-end applied skills: building autonomous agentic systems, applying RAG pipelines, using planning agents, orchestration frameworks, tool-calling, and developing real-world multimodal AI applications.